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Bias, see page3352, can exist in decision thresholds, e.g. threshold setting in classification. In binary classification, a common practice is to set a threshold at 0.5 to decide between two classes. However, this might not be fair for all groups.  

Bias Introduction: Using a single threshold might lead to higher false positive rates for some groups compared to others if the data distribution varies across groups. 

Mitigation of bias:

• Adjust the decision threshold for different groups based on ROC curves or performance metrics that consider both fairness and accuracy.
• Use cost-sensitive learning, where different costs are assigned to misclassifications for different groups, balancing the rates of false positives and negatives.

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